SWIFT: Scalable Weighted Iterative Flow-clustering Technique  



Flow cytometry (FC) is a powerful technology for rapid multivariate analysis of individual cells. Flow cytometers today can readily measure 20 or more attributes per cell for datasets with millions of cells. The SWIFT routines provided here provide tools for automating analysis for such massive high-dimensional datasets.

SWIFT is based on a multi-stage framework for clustering with features that are motivated in particular by the characteristics of FC data. In particular, SWIFT aims to identify rare populations that are commonly of interest in immunological studies and attempts to honor the modality of the data by ensuring that multi-modal clusters are not grouped together within individual clusters. The current version of the SWIFT software routines and publications providing some technical details are accessible through the links provided below. Development and refinement of SWIFT is continuing and we anticipate updates to this page as newer versions of SWIFT and further publications become available.


Request SWIFT 3.0

Video Tutorials for SWIFT Users (YouTube)

Selected Publications

  1. J. A. Rebhahn, D. R. Roumanes, Y. Qi, A. Khan, J. Thakar, A. Rosenberg, F. E.-H. Lee, S. A. Quataert, G. Sharma, and T. R. Mosmann,; "Competitive SWIFT cluster templates enhance detection of aging changes"; Cytometry, Part A; vol. 89, no. 1, pp. 59-70, Jan 2016, doi: 10.1002/cyto.a.22740. [Open Access]
  2. I. Naim, S. Datta, J. Rebhahn, J. S. Cavenaugh, T. R. Mosmann, and G. Sharma,; "SWIFT - scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets: Part 1 - Algorithm design"; Cytometry, Part A; vol. 85, no. 5, pp. 408-421, May 2014. [Open Access]
  3. T. R. Mosmann, I. Naim, J. Rebhahn, S. Datta, J. S. Cavenaugh, J. M. Weaver, and G. Sharma,; "SWIFT - scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets: Part 2 - Biological evaluation"; Cytometry, Part A; vol. 85, no. 5, pp. 422-433, May 2014. [Open Access]
  4. Iftekhar Naim, Suprakash Datta, Gaurav Sharma, James S. Cavenaugh, Tim R. Mosmann; "SWIFT: Scalable weighted iterative sampling for flow cytometry clustering"; IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP); March 2010, pp. 509--512. [pdf]

Also of Potential Interest

    If you find the SWIFT set of tools to be useful and are interested in software for related applications from our group, please also see:
  1. LAVA:Landscape Animation for Visualizing Attractors[LAVA Web Page]

Disclaimer: We share the SWIFT source code in the hope that it will be useful to the flow cytometry community. The software comes with no warranties what so ever. We try to ensure that the SWIFT software can be used "out of the box" and try and respond to user reports of problems and queries but do understand that our response time can vary.